Semi-supervised learning based edge-preserving background estimation for small target detection

• Semi-supervised learning is used in background estimation for small target detection.
• Geometrical structures in patch image are described using Graph Laplacian.
• Graph Laplacian regularization is incorporated in the semi-supervised learning model.
• Bilateral kernel is utilized to realize background estimation method.

The edges in infrared image will cause false alarms in small target detection. So a novel edge-preserving background estimation method is proposed in this paper for single frame small target detection. First, we propose a novel background estimation method based on semi-supervised learning, and the Graph Laplacian regularization is utilized in this model to preserve accurate edges in estimated background image. Then, the bilateral kernel is utilized to realize background estimation method. At last, edge-preserving estimated background is eliminated from original image to get difference image which is used as foreground to detect the small target. The experiment results demonstrate that our proposed method can achieve edge-preserving background estimation significantly and efficiently, and get better small target detection results.